Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations899164
Missing cells742002
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory171.5 MiB
Average record size in memory200.0 B

Variable types

Numeric10
Text6
DateTime3
Categorical6

Dataset

DescriptionThis profiling report was copied from 2025 Misiriya's KaggleX BIPOC project
URL

Alerts

ApprovalFY is highly overall correlated with RetainedJobHigh correlation
GrAppv is highly overall correlated with TermHigh correlation
RetainedJob is highly overall correlated with ApprovalFYHigh correlation
Term is highly overall correlated with GrAppvHigh correlation
FranchiseCode is highly imbalanced (68.2%) Imbalance
BalanceGross is highly imbalanced (> 99.9%) Imbalance
ChgOffDate has 736465 (81.9%) missing values Missing
NoEmp is highly skewed (γ1 = 80.24824355) Skewed
CreateJob is highly skewed (γ1 = 36.99135473) Skewed
RetainedJob is highly skewed (γ1 = 36.85481184) Skewed
LoanNr_ChkDgt has unique values Unique
NAICS has 201948 (22.5%) zeros Zeros
CreateJob has 629248 (70.0%) zeros Zeros
RetainedJob has 440403 (49.0%) zeros Zeros

Reproduction

Analysis started2025-02-13 23:22:47.571444
Analysis finished2025-02-13 23:23:59.980995
Duration1 minute and 12.41 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

LoanNr_ChkDgt
Real number (ℝ)

Unique 

Distinct899164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7726123 × 109
Minimum1.000014 × 109
Maximum9.996003 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:00.092945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.000014 × 109
5-th percentile1.3484572 × 109
Q12.5897575 × 109
median4.361439 × 109
Q36.9046265 × 109
95-th percentile9.1648039 × 109
Maximum9.996003 × 109
Range8.995989 × 109
Interquartile range (IQR)4.314869 × 109

Descriptive statistics

Standard deviation2.538175 × 109
Coefficient of variation (CV)0.53182091
Kurtosis-1.086499
Mean4.7726123 × 109
Median Absolute Deviation (MAD)2.0134 × 109
Skewness0.3647571
Sum4.2913612 × 1015
Variance6.4423325 × 1018
MonotonicityStrictly increasing
2025-02-14T00:24:00.196888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000014003 1
 
< 0.1%
5944984007 1
 
< 0.1%
5944874009 1
 
< 0.1%
5944884001 1
 
< 0.1%
5944904005 1
 
< 0.1%
5944914008 1
 
< 0.1%
5944924000 1
 
< 0.1%
5944934003 1
 
< 0.1%
5944944006 1
 
< 0.1%
5944954009 1
 
< 0.1%
Other values (899154) 899154
> 99.9%
ValueCountFrequency (%)
1000014003 1
< 0.1%
1000024006 1
< 0.1%
1000034009 1
< 0.1%
1000044001 1
< 0.1%
1000054004 1
< 0.1%
1000084002 1
< 0.1%
1000093009 1
< 0.1%
1000094005 1
< 0.1%
1000104006 1
< 0.1%
1000124001 1
< 0.1%
ValueCountFrequency (%)
9996003010 1
< 0.1%
9995973006 1
< 0.1%
9995613003 1
< 0.1%
9995603000 1
< 0.1%
9995573004 1
< 0.1%
9995563001 1
< 0.1%
9995493004 1
< 0.1%
9995473009 1
< 0.1%
9995453003 1
< 0.1%
9995423005 1
< 0.1%

Name
Text

Distinct779583
Distinct (%)86.7%
Missing14
Missing (%)< 0.1%
Memory size6.9 MiB
2025-02-14T00:24:00.595271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length23
Mean length21.775963
Min length1

Characters and Unicode

Total characters19579857
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique706468 ?
Unique (%)78.6%

Sample

1st rowABC HOBBYCRAFT
2nd rowLANDMARK BAR & GRILLE (THE)
3rd rowWHITLOCK DDS, TODD M.
4th rowBIG BUCKS PAWN & JEWELRY, LLC
5th rowANASTASIA CONFECTIONS, INC.
ValueCountFrequency (%)
inc 263379
 
8.4%
100280
 
3.2%
llc 77826
 
2.5%
and 28959
 
0.9%
the 28389
 
0.9%
of 23026
 
0.7%
dba 20214
 
0.6%
co 18216
 
0.6%
a 18114
 
0.6%
services 17318
 
0.6%
Other values (226643) 2530176
80.9%
2025-02-14T00:24:01.051624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19579857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2231639
 
11.4%
E 1354056
 
6.9%
I 1226719
 
6.3%
A 1177821
 
6.0%
N 1170319
 
6.0%
R 1052562
 
5.4%
C 1038114
 
5.3%
S 1009495
 
5.2%
O 933206
 
4.8%
T 917437
 
4.7%
Other values (81) 7468489
38.1%

City
Text

Distinct32581
Distinct (%)3.6%
Missing30
Missing (%)< 0.1%
Memory size6.9 MiB
2025-02-14T00:24:01.246979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length9.1030625
Min length1

Characters and Unicode

Total characters8184873
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12872 ?
Unique (%)1.4%

Sample

1st rowEVANSVILLE
2nd rowNEW PARIS
3rd rowBLOOMINGTON
4th rowBROKEN ARROW
5th rowORLANDO
ValueCountFrequency (%)
city 23831
 
2.0%
san 21942
 
1.8%
new 16075
 
1.3%
los 13000
 
1.1%
angeles 12380
 
1.0%
lake 10729
 
0.9%
houston 10587
 
0.9%
beach 10462
 
0.9%
park 10316
 
0.9%
york 9724
 
0.8%
Other values (17695) 1066583
88.5%
2025-02-14T00:24:01.536929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8184873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8184873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8184873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 744405
 
9.1%
E 723098
 
8.8%
O 632510
 
7.7%
N 621338
 
7.6%
L 573578
 
7.0%
R 513614
 
6.3%
S 475392
 
5.8%
I 468344
 
5.7%
T 425108
 
5.2%
306936
 
3.8%
Other values (70) 2700550
33.0%

State
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1199.5479
Minimum111
Maximum4633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:01.618452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile301
Q1612
median1314
Q31518
95-th percentile2301
Maximum4633
Range4522
Interquartile range (IQR)906

Descriptive statistics

Standard deviation648.31203
Coefficient of variation (CV)0.54046362
Kurtosis-1.0275286
Mean1199.5479
Median Absolute Deviation (MAD)495
Skewness-0.07158061
Sum1.0785903 × 109
Variance420308.48
MonotonicityNot monotonic
2025-02-14T00:24:01.710202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301 130619
 
14.5%
2024 70458
 
7.8%
1425 57693
 
6.4%
612 41212
 
4.6%
1601 35170
 
3.9%
1508 32622
 
3.6%
912 29669
 
3.3%
1301 25272
 
2.8%
1314 24373
 
2.7%
1410 24035
 
2.7%
Other values (42) 428041
47.6%
ValueCountFrequency (%)
111 2405
 
0.3%
112 8362
 
0.9%
118 6341
 
0.7%
126 17631
 
2.0%
301 130619
14.5%
315 20605
 
2.3%
320 12229
 
1.4%
403 1613
 
0.2%
405 2220
 
0.2%
612 41212
 
4.6%
ValueCountFrequency (%)
4633 14
 
< 0.1%
2325 2839
 
0.3%
2322 3287
 
0.4%
2309 21040
 
2.3%
2301 23263
 
2.6%
2220 5454
 
0.6%
2201 13264
 
1.5%
2120 18776
 
2.1%
2024 70458
7.8%
2014 9403
 
1.0%

Zip
Real number (ℝ)

Distinct33611
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53804.391
Minimum0
Maximum99999
Zeros283
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:01.800172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3838
Q127587
median55410
Q383704
95-th percentile95822
Maximum99999
Range99999
Interquartile range (IQR)56117

Descriptive statistics

Standard deviation31184.159
Coefficient of variation (CV)0.5795839
Kurtosis-1.3359893
Mean53804.391
Median Absolute Deviation (MAD)28206
Skewness-0.16816663
Sum4.8378972 × 1010
Variance9.7245178 × 108
MonotonicityNot monotonic
2025-02-14T00:24:01.889303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 933
 
0.1%
90015 926
 
0.1%
93401 806
 
0.1%
90010 733
 
0.1%
33166 671
 
0.1%
90021 666
 
0.1%
59601 640
 
0.1%
65804 599
 
0.1%
3801 581
 
0.1%
59101 578
 
0.1%
Other values (33601) 892031
99.2%
ValueCountFrequency (%)
0 283
< 0.1%
1 24
 
< 0.1%
2 11
 
< 0.1%
3 5
 
< 0.1%
4 5
 
< 0.1%
5 5
 
< 0.1%
6 4
 
< 0.1%
7 6
 
< 0.1%
8 15
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
99999 209
< 0.1%
99950 3
 
< 0.1%
99929 15
 
< 0.1%
99928 1
 
< 0.1%
99926 1
 
< 0.1%
99925 4
 
< 0.1%
99923 1
 
< 0.1%
99921 13
 
< 0.1%
99919 2
 
< 0.1%
99918 1
 
< 0.1%

Bank
Text

Distinct5802
Distinct (%)0.6%
Missing1559
Missing (%)0.2%
Memory size6.9 MiB
2025-02-14T00:24:02.064483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length26
Mean length23.187946
Min length3

Characters and Unicode

Total characters20813616
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique923 ?
Unique (%)0.1%

Sample

1st rowFIFTH THIRD BANK
2nd row1ST SOURCE BANK
3rd rowGRANT COUNTY STATE BANK
4th row1ST NATL BK & TR CO OF BROKEN
5th rowFLORIDA BUS. DEVEL CORP
ValueCountFrequency (%)
bank 651608
18.5%
natl 318240
 
9.0%
assoc 306768
 
8.7%
of 142852
 
4.1%
national 125899
 
3.6%
america 100686
 
2.9%
association 84965
 
2.4%
fargo 63732
 
1.8%
wells 63650
 
1.8%
52264
 
1.5%
Other values (3602) 1606709
45.7%
2025-02-14T00:24:02.321897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20813616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20813616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20813616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2762231
13.3%
2620014
12.6%
N 2105500
10.1%
S 1520499
 
7.3%
O 1336993
 
6.4%
T 1181841
 
5.7%
C 1134642
 
5.5%
I 1061717
 
5.1%
E 923739
 
4.4%
L 922583
 
4.4%
Other values (40) 5243857
25.2%
Distinct56
Distinct (%)< 0.1%
Missing1566
Missing (%)0.2%
Memory size6.9 MiB
2025-02-14T00:24:02.431158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1795196
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowOH
2nd rowIN
3rd rowIN
4th rowOK
5th rowFL
ValueCountFrequency (%)
ca 118116
 
13.2%
nc 79514
 
8.9%
il 65908
 
7.3%
oh 58461
 
6.5%
sd 51095
 
5.7%
tx 47790
 
5.3%
ri 45366
 
5.1%
ny 39592
 
4.4%
va 29002
 
3.2%
de 24537
 
2.7%
Other values (46) 338217
37.7%
2025-02-14T00:24:02.595587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1795196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1795196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1795196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 241398
13.4%
C 229604
12.8%
N 187751
10.5%
I 158854
 
8.8%
O 102604
 
5.7%
L 96914
 
5.4%
D 96078
 
5.4%
T 94941
 
5.3%
M 85034
 
4.7%
S 73385
 
4.1%
Other values (14) 428633
23.9%

NAICS
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.612263
Minimum0
Maximum92
Zeros201948
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:02.661044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median44
Q356
95-th percentile81
Maximum92
Range92
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.284706
Coefficient of variation (CV)0.66354972
Kurtosis-1.0572678
Mean39.612263
Median Absolute Deviation (MAD)18
Skewness-0.24819754
Sum35617921
Variance690.88577
MonotonicityNot monotonic
2025-02-14T00:24:02.726960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 201948
22.5%
44 84737
9.4%
81 72618
 
8.1%
54 68170
 
7.6%
72 67600
 
7.5%
23 66646
 
7.4%
62 55366
 
6.2%
42 48743
 
5.4%
45 42514
 
4.7%
33 38284
 
4.3%
Other values (15) 152538
17.0%
ValueCountFrequency (%)
0 201948
22.5%
11 9005
 
1.0%
21 1851
 
0.2%
22 663
 
0.1%
23 66646
 
7.4%
31 11809
 
1.3%
32 17936
 
2.0%
33 38284
 
4.3%
42 48743
 
5.4%
44 84737
9.4%
ValueCountFrequency (%)
92 229
 
< 0.1%
81 72618
8.1%
72 67600
7.5%
71 14640
 
1.6%
62 55366
6.2%
61 6425
 
0.7%
56 32685
3.6%
55 257
 
< 0.1%
54 68170
7.6%
53 13632
 
1.5%
Distinct9859
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
Minimum1975-01-20 00:00:00
Maximum2074-12-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-14T00:24:02.805616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:24:02.894399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ApprovalFY
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.1436
Minimum1962
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:02.982382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1962
5-th percentile1991
Q11997
median2002
Q32006
95-th percentile2009
Maximum2014
Range52
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9138459
Coefficient of variation (CV)0.0029552332
Kurtosis-0.092531047
Mean2001.1436
Median Absolute Deviation (MAD)4
Skewness-0.58537855
Sum1.7993562 × 109
Variance34.973573
MonotonicityNot monotonic
2025-02-14T00:24:03.165634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 77525
 
8.6%
2006 76040
 
8.5%
2007 71876
 
8.0%
2004 68290
 
7.6%
2003 58193
 
6.5%
1995 45758
 
5.1%
2002 44391
 
4.9%
1996 40112
 
4.5%
2008 39540
 
4.4%
1997 37748
 
4.2%
Other values (41) 339691
37.8%
ValueCountFrequency (%)
1962 1
 
< 0.1%
1965 1
 
< 0.1%
1966 1
 
< 0.1%
1967 2
 
< 0.1%
1968 2
 
< 0.1%
1969 4
 
< 0.1%
1970 8
 
< 0.1%
1971 20
 
< 0.1%
1972 27
< 0.1%
1973 52
< 0.1%
ValueCountFrequency (%)
2014 268
 
< 0.1%
2013 2458
 
0.3%
2012 5997
 
0.7%
2011 12608
 
1.4%
2010 16848
 
1.9%
2009 19126
 
2.1%
2008 39540
4.4%
2007 71876
8.0%
2006 76040
8.5%
2005 77525
8.6%

Term
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.77308
Minimum0
Maximum569
Zeros810
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:03.250881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q160
median84
Q3120
95-th percentile300
Maximum569
Range569
Interquartile range (IQR)60

Descriptive statistics

Standard deviation78.857305
Coefficient of variation (CV)0.7118815
Kurtosis0.18570424
Mean110.77308
Median Absolute Deviation (MAD)33
Skewness1.1209258
Sum99603164
Variance6218.4746
MonotonicityNot monotonic
2025-02-14T00:24:03.337545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 230162
25.6%
60 89945
 
10.0%
240 85982
 
9.6%
120 77654
 
8.6%
300 44727
 
5.0%
180 28164
 
3.1%
36 19800
 
2.2%
12 17095
 
1.9%
48 15621
 
1.7%
72 9419
 
1.0%
Other values (402) 280595
31.2%
ValueCountFrequency (%)
0 810
 
0.1%
1 1608
0.2%
2 1809
0.2%
3 2112
0.2%
4 2173
0.2%
5 1866
0.2%
6 3054
0.3%
7 1761
0.2%
8 1693
0.2%
9 1875
0.2%
ValueCountFrequency (%)
569 1
< 0.1%
527 1
< 0.1%
511 1
< 0.1%
505 1
< 0.1%
481 1
< 0.1%
480 1
< 0.1%
461 1
< 0.1%
449 1
< 0.1%
445 1
< 0.1%
443 1
< 0.1%

NoEmp
Real number (ℝ)

Skewed 

Distinct599
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.411353
Minimum0
Maximum9999
Zeros6631
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:03.427109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile40
Maximum9999
Range9999
Interquartile range (IQR)8

Descriptive statistics

Standard deviation74.108196
Coefficient of variation (CV)6.4942514
Kurtosis7965.2886
Mean11.411353
Median Absolute Deviation (MAD)3
Skewness80.248244
Sum10260678
Variance5492.0248
MonotonicityNot monotonic
2025-02-14T00:24:03.516133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
 
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
10 31536
 
3.5%
7 31495
 
3.5%
8 31361
 
3.5%
12 20822
 
2.3%
Other values (589) 221003
24.6%
ValueCountFrequency (%)
0 6631
 
0.7%
1 154254
17.2%
2 138297
15.4%
3 90674
10.1%
4 73644
8.2%
5 60319
 
6.7%
6 45759
 
5.1%
7 31495
 
3.5%
8 31361
 
3.5%
9 18131
 
2.0%
ValueCountFrequency (%)
9999 4
< 0.1%
9992 1
 
< 0.1%
9945 1
 
< 0.1%
9090 1
 
< 0.1%
9000 2
 
< 0.1%
8500 1
 
< 0.1%
8041 1
 
< 0.1%
8018 1
 
< 0.1%
8000 7
< 0.1%
7999 1
 
< 0.1%

NewExist
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1
644869 
2
253125 
0
 
1170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 644869
71.7%
2 253125
 
28.2%
0 1170
 
0.1%

Length

2025-02-14T00:24:03.589108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T00:24:03.645288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 644869
71.7%
2 253125
 
28.2%
0 1170
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 644869
71.7%
2 253125
 
28.2%
0 1170
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 644869
71.7%
2 253125
 
28.2%
0 1170
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 644869
71.7%
2 253125
 
28.2%
0 1170
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 644869
71.7%
2 253125
 
28.2%
0 1170
 
0.1%

CreateJob
Real number (ℝ)

Skewed  Zeros 

Distinct246
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4303764
Minimum0
Maximum8800
Zeros629248
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:03.711591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum8800
Range8800
Interquartile range (IQR)1

Descriptive statistics

Standard deviation236.68817
Coefficient of variation (CV)28.075634
Kurtosis1369.911
Mean8.4303764
Median Absolute Deviation (MAD)0
Skewness36.991355
Sum7580291
Variance56021.288
MonotonicityNot monotonic
2025-02-14T00:24:03.797696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
10 11602
 
1.3%
6 11009
 
1.2%
8 7378
 
0.8%
7 6374
 
0.7%
Other values (236) 44540
 
5.0%
ValueCountFrequency (%)
0 629248
70.0%
1 63174
 
7.0%
2 57831
 
6.4%
3 28806
 
3.2%
4 20511
 
2.3%
5 18691
 
2.1%
6 11009
 
1.2%
7 6374
 
0.7%
8 7378
 
0.8%
9 3330
 
0.4%
ValueCountFrequency (%)
8800 648
0.1%
5621 1
 
< 0.1%
5199 1
 
< 0.1%
5085 1
 
< 0.1%
3500 1
 
< 0.1%
3100 1
 
< 0.1%
3000 4
 
< 0.1%
2515 1
 
< 0.1%
2140 1
 
< 0.1%
2020 1
 
< 0.1%

RetainedJob
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct358
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.797257
Minimum0
Maximum9500
Zeros440403
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:03.883528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile20
Maximum9500
Range9500
Interquartile range (IQR)4

Descriptive statistics

Standard deviation237.1206
Coefficient of variation (CV)21.961188
Kurtosis1362.0182
Mean10.797257
Median Absolute Deviation (MAD)1
Skewness36.854812
Sum9708505
Variance56226.179
MonotonicityNot monotonic
2025-02-14T00:24:03.969989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
10 15438
 
1.7%
Other values (348) 99402
 
11.1%
ValueCountFrequency (%)
0 440403
49.0%
1 88790
 
9.9%
2 76851
 
8.5%
3 49963
 
5.6%
4 39666
 
4.4%
5 32627
 
3.6%
6 23796
 
2.6%
7 16530
 
1.8%
8 15698
 
1.7%
9 8735
 
1.0%
ValueCountFrequency (%)
9500 1
 
< 0.1%
8800 648
0.1%
7250 1
 
< 0.1%
5000 1
 
< 0.1%
4441 1
 
< 0.1%
4000 2
 
< 0.1%
3900 1
 
< 0.1%
3860 1
 
< 0.1%
3225 1
 
< 0.1%
3200 1
 
< 0.1%

FranchiseCode
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
0
847389 
1
 
51775

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 847389
94.2%
1 51775
 
5.8%

Length

2025-02-14T00:24:04.043116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T00:24:04.081701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 847389
94.2%
1 51775
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 847389
94.2%
1 51775
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 847389
94.2%
1 51775
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 847389
94.2%
1 51775
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 847389
94.2%
1 51775
 
5.8%

RevLineCr
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
1
420288 
0
262195 
3
201397 
2
 
15284

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 420288
46.7%
0 262195
29.2%
3 201397
22.4%
2 15284
 
1.7%

Length

2025-02-14T00:24:04.127774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T00:24:04.174309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 420288
46.7%
0 262195
29.2%
3 201397
22.4%
2 15284
 
1.7%

Most occurring characters

ValueCountFrequency (%)
1 420288
46.7%
0 262195
29.2%
3 201397
22.4%
2 15284
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 420288
46.7%
0 262195
29.2%
3 201397
22.4%
2 15284
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 420288
46.7%
0 262195
29.2%
3 201397
22.4%
2 15284
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 420288
46.7%
0 262195
29.2%
3 201397
22.4%
2 15284
 
1.7%

LowDoc
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
0
788829 
1
110335 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters899164
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Length

2025-02-14T00:24:04.232661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T00:24:04.273594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 899164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 788829
87.7%
1 110335
 
12.3%

ChgOffDate
Date

Missing 

Distinct6448
Distinct (%)4.0%
Missing736465
Missing (%)81.9%
Memory size6.9 MiB
Minimum1988-10-03 00:00:00
Maximum2026-10-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-14T00:24:04.335216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:24:04.431975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct8472
Distinct (%)0.9%
Missing2368
Missing (%)0.3%
Memory size6.9 MiB
Minimum1975-01-17 00:00:00
Maximum2074-12-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-14T00:24:04.519195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:24:04.608471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct118859
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:04.808467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.537586
Min length6

Characters and Unicode

Total characters10374182
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79785 ?
Unique (%)8.9%

Sample

1st row$60,000.00
2nd row$40,000.00
3rd row$287,000.00
4th row$35,000.00
5th row$229,000.00
ValueCountFrequency (%)
50,000.00 43787
 
4.9%
100,000.00 36714
 
4.1%
25,000.00 27387
 
3.0%
150,000.00 23373
 
2.6%
10,000.00 21328
 
2.4%
35,000.00 14748
 
1.6%
5,000.00 14193
 
1.6%
75,000.00 13528
 
1.5%
20,000.00 13462
 
1.5%
30,000.00 12696
 
1.4%
Other values (118849) 677948
75.4%
2025-02-14T00:24:05.068473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
$ 899164
 
8.7%
. 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10374182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
$ 899164
 
8.7%
. 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10374182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
$ 899164
 
8.7%
. 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10374182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4457089
43.0%
, 924978
 
8.9%
$ 899164
 
8.7%
. 899164
 
8.7%
899164
 
8.7%
5 445569
 
4.3%
1 409947
 
4.0%
2 312909
 
3.0%
3 238773
 
2.3%
4 207077
 
2.0%
Other values (4) 680348
 
6.6%

BalanceGross
Categorical

Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
$0.00
899150 
$12,750.00
 
1
$827,875.00
 
1
$25,000.00
 
1
$37,100.00
 
1
Other values (10)
 
10

Length

Max length12
Median length6
Mean length6.0000767
Min length6

Characters and Unicode

Total characters5395053
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row$0.00
2nd row$0.00
3rd row$0.00
4th row$0.00
5th row$0.00

Common Values

ValueCountFrequency (%)
$0.00 899150
> 99.9%
$12,750.00 1
 
< 0.1%
$827,875.00 1
 
< 0.1%
$25,000.00 1
 
< 0.1%
$37,100.00 1
 
< 0.1%
$43,127.00 1
 
< 0.1%
$84,617.00 1
 
< 0.1%
$1,760.00 1
 
< 0.1%
$115,820.00 1
 
< 0.1%
$996,262.00 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Length

2025-02-14T00:24:05.146143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.00 899150
> 99.9%
12,750.00 1
 
< 0.1%
827,875.00 1
 
< 0.1%
25,000.00 1
 
< 0.1%
37,100.00 1
 
< 0.1%
43,127.00 1
 
< 0.1%
84,617.00 1
 
< 0.1%
1,760.00 1
 
< 0.1%
115,820.00 1
 
< 0.1%
996,262.00 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5395053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5395053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5395053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2697490
50.0%
$ 899164
 
16.7%
. 899164
 
16.7%
899164
 
16.7%
, 13
 
< 0.1%
1 11
 
< 0.1%
7 8
 
< 0.1%
2 7
 
< 0.1%
6 7
 
< 0.1%
9 7
 
< 0.1%
Other values (4) 18
 
< 0.1%

MIS_Status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
P I F
741345 
CHGOFF
157819 

Length

Max length6
Median length5
Mean length5.1755175
Min length5

Characters and Unicode

Total characters4653639
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP I F
2nd rowP I F
3rd rowP I F
4th rowP I F
5th rowP I F

Common Values

ValueCountFrequency (%)
P I F 741345
82.4%
CHGOFF 157819
 
17.6%

Length

2025-02-14T00:24:05.215119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-14T00:24:05.260608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
p 741345
31.1%
i 741345
31.1%
f 741345
31.1%
chgoff 157819
 
6.6%

Most occurring characters

ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4653639
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1482690
31.9%
F 1056983
22.7%
P 741345
15.9%
I 741345
15.9%
C 157819
 
3.4%
H 157819
 
3.4%
G 157819
 
3.4%
O 157819
 
3.4%
Distinct83165
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:05.439356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.8997235
Min length6

Characters and Unicode

Total characters6203983
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52342 ?
Unique (%)5.8%

Sample

1st row$0.00
2nd row$0.00
3rd row$0.00
4th row$0.00
5th row$0.00
ValueCountFrequency (%)
0.00 737152
82.0%
50,000.00 2110
 
0.2%
10,000.00 1865
 
0.2%
25,000.00 1371
 
0.2%
35,000.00 1345
 
0.1%
100,000.00 1028
 
0.1%
20,000.00 594
 
0.1%
30,000.00 492
 
0.1%
15,000.00 467
 
0.1%
5,000.00 356
 
< 0.1%
Other values (83155) 152384
 
16.9%
2025-02-14T00:24:05.725450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6203983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6203983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6203983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2643222
42.6%
$ 899164
 
14.5%
. 899164
 
14.5%
899164
 
14.5%
, 161591
 
2.6%
1 98607
 
1.6%
2 88727
 
1.4%
4 86077
 
1.4%
9 81470
 
1.3%
3 79226
 
1.3%
Other values (4) 267571
 
4.3%

GrAppv
Real number (ℝ)

High correlation 

Distinct22128
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192686.98
Minimum200
Maximum5472000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 MiB
2025-02-14T00:24:05.810194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile10000
Q135000
median90000
Q3225000
95-th percentile750000
Maximum5472000
Range5471800
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation283263.39
Coefficient of variation (CV)1.4700702
Kurtosis21.018882
Mean192686.98
Median Absolute Deviation (MAD)65000
Skewness3.5207901
Sum1.7325719 × 1011
Variance8.0238149 × 1010
MonotonicityNot monotonic
2025-02-14T00:24:05.900081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 69394
 
7.7%
25000 51258
 
5.7%
100000 50977
 
5.7%
10000 38366
 
4.3%
150000 27624
 
3.1%
20000 23434
 
2.6%
35000 23181
 
2.6%
30000 21004
 
2.3%
5000 19146
 
2.1%
15000 18472
 
2.1%
Other values (22118) 556308
61.9%
ValueCountFrequency (%)
200 2
 
< 0.1%
300 1
 
< 0.1%
400 2
 
< 0.1%
500 33
 
< 0.1%
700 4
 
< 0.1%
800 4
 
< 0.1%
950 1
 
< 0.1%
1000 444
< 0.1%
1200 12
 
< 0.1%
1300 2
 
< 0.1%
ValueCountFrequency (%)
5472000 1
 
< 0.1%
5000000 40
< 0.1%
4991700 1
 
< 0.1%
4950000 1
 
< 0.1%
4908500 1
 
< 0.1%
4900000 2
 
< 0.1%
4872000 1
 
< 0.1%
4869000 1
 
< 0.1%
4830000 1
 
< 0.1%
4800000 1
 
< 0.1%

Interactions

2025-02-14T00:23:54.166594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.007762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.418623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.767815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.145345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.489228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.849403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.301619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.606235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.894449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.301513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.148487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.552176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.908150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.282003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.623096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.990946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.429093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.757138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.025096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.436436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.282623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.683899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.044378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.412576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.760502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:49.213722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.557050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.885185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.148715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.570285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.412356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.821208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.184014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.543005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.900379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:49.348583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.687336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.012930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.278605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.702449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.625181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.956902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.321079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.691529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.029647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:49.485741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.817041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.138222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.403629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.837587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.761284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.094555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.459229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.826792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.168511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:49.621917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.949620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.266327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.532409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.969731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:42.895399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.226857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.597238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.957549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.303508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:49.760902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.082258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.395110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.656703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:55.103954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.024123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.363381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.733616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.092566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.437184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:49.896806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.215760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.518756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.784638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:55.233701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.151242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.497061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:45.870374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.222764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.573053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.032181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.342491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.642313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:53.907782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:55.366944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:43.280722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:44.631229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:46.009204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:47.355045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:48.709949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:50.169107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:51.473888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:52.765814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-14T00:23:54.033772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-14T00:24:05.973970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ApprovalFYBalanceGrossCreateJobFranchiseCodeGrAppvLoanNr_ChkDgtLowDocMIS_StatusNAICSNewExistNoEmpRetainedJobRevLineCrStateTermZip
ApprovalFY1.0000.0000.2680.048-0.300-0.2780.3750.3270.4400.062-0.2260.5460.359-0.001-0.297-0.038
BalanceGross0.0001.0000.0000.0050.0000.0010.0000.0000.0010.0000.0000.0000.0060.0000.0000.001
CreateJob0.2680.0001.0000.0010.093-0.0310.0100.0120.1560.0090.0340.3770.016-0.0320.0820.026
FranchiseCode0.0480.0050.0011.0000.0650.0570.0280.0150.2220.1420.0020.0040.1290.0360.1050.066
GrAppv-0.3000.0000.0930.0651.0000.1390.1160.074-0.1420.0370.455-0.1380.099-0.0670.5580.119
LoanNr_ChkDgt-0.2780.001-0.0310.0570.1391.0000.2470.237-0.0470.0620.075-0.1420.1460.0070.1210.031
LowDoc0.3750.0000.0100.0280.1160.2471.0000.0840.1540.1610.0030.0100.2260.0870.1690.145
MIS_Status0.3270.0000.0120.0150.0740.2370.0841.0000.1480.0220.0040.0130.1460.0540.4910.080
NAICS0.4400.0010.1560.222-0.142-0.0470.1540.1481.0000.093-0.1510.2680.214-0.000-0.076-0.033
NewExist0.0620.0000.0090.1420.0370.0620.1610.0220.0931.0000.0040.0020.0650.0700.0880.088
NoEmp-0.2260.0000.0340.0020.4550.0750.0030.004-0.1510.0041.0000.1240.005-0.0400.2000.059
RetainedJob0.5460.0000.3770.004-0.138-0.1420.0100.0130.2680.0020.1241.0000.016-0.030-0.157-0.026
RevLineCr0.3590.0060.0160.1290.0990.1460.2260.1460.2140.0650.0050.0161.0000.0460.2420.096
State-0.0010.000-0.0320.036-0.0670.0070.0870.054-0.0000.070-0.040-0.0300.0461.000-0.088-0.240
Term-0.2970.0000.0820.1050.5580.1210.1690.491-0.0760.0880.200-0.1570.242-0.0881.0000.142
Zip-0.0380.0010.0260.0660.1190.0310.1450.080-0.0330.0880.059-0.0260.096-0.2400.1421.000

Missing values

2025-02-14T00:23:55.893623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-14T00:23:57.066658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-14T00:23:59.153975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFYTermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppv
01000014003ABC HOBBYCRAFTEVANSVILLE91447711FIFTH THIRD BANKOH4528-Feb-971997844200011NaN28-Feb-99$60,000.00$0.00P I F$0.0060000
11000024006LANDMARK BAR & GRILLE (THE)NEW PARIS914465261ST SOURCE BANKIN7228-Feb-971997602200011NaN31-May-97$40,000.00$0.00P I F$0.0040000
21000034009WHITLOCK DDS, TODD M.BLOOMINGTON91447401GRANT COUNTY STATE BANKIN6228-Feb-9719971807100010NaN31-Dec-97$287,000.00$0.00P I F$0.00287000
31000044001BIG BUCKS PAWN & JEWELRY, LLCBROKEN ARROW1511740121ST NATL BK & TR CO OF BROKENOK028-Feb-971997602100011NaN30-Jun-97$35,000.00$0.00P I F$0.0035000
41000054004ANASTASIA CONFECTIONS, INC.ORLANDO61232801FLORIDA BUS. DEVEL CORPFL028-Feb-97199724014177010NaN14-May-97$229,000.00$0.00P I F$0.00229000
51000084002B&T SCREW MACHINE COMPANY, INCPLAINVILLE3206062TD BANK, NATIONAL ASSOCIATIONDE3328-Feb-97199712019100010NaN30-Jun-97$517,000.00$0.00P I F$0.00517000
61000093009MIDDLE ATLANTIC SPORTS CO INCUNION14107083WELLS FARGO BANK NATL ASSOCSD02-Jun-801980454520001024-Jun-9122-Jul-80$600,000.00$0.00CHGOFF$208,959.00600000
71000094005WEAVER PRODUCTSSUMMERFIELD61234491REGIONS BANKAL8128-Feb-971997841200011NaN30-Jun-98$45,000.00$0.00P I F$0.0045000
81000104006TURTLE BEACH INNPORT SAINT JOE61232456CENTENNIAL BANKFL7228-Feb-9719972972200010NaN31-Jul-97$305,000.00$0.00P I F$0.00305000
91000124001INTEXT BUILDING SYS LLCGLASTONBURY3206073WEBSTER BANK NATL ASSOCCT028-Feb-971997843200011NaN30-Apr-97$70,000.00$0.00P I F$0.0070000
LoanNr_ChkDgtNameCityStateZipBankBankStateNAICSApprovalDateApprovalFYTermNoEmpNewExistCreateJobRetainedJobFranchiseCodeRevLineCrLowDocChgOffDateDisbursementDateDisbursementGrossBalanceGrossMIS_StatusChgOffPrinGrGrAppv
8991549995423005LITWIN LIVERY SERVICES, INC.CAMPBELL150844405JPMORGAN CHASE BANK NATL ASSOCIL027-Feb-971997601100000NaN30-Sep-97$10,000.00$0.00P I F$0.0010000
8991559995453003FUTURE LEADERS CENTER, INC.SO. OZONE PARK142511420FLUSHING BANKNY6227-Feb-9719971802100000NaN30-Jun-97$123,000.00$0.00P I F$0.00128000
8991569995473009FABRICATORS STEEL, INC.BALTIMORE130421224BANK OF AMERICA NATL ASSOCMD3327-Feb-9719976020100000NaN30-Jun-97$50,000.00$0.00P I F$0.0050000
8991579995493004PULLTARPS MFG.EL CAJON30192020U.S. BANK NATIONAL ASSOCIATIONCA3127-Feb-9719973640100010NaN31-Mar-97$200,000.00$0.00P I F$0.00200000
8991589995563001SHADES WINDOW TINTING AUTO ALAIRVING202475062LOANS FROM OLD CLOSED LENDERSDC027-Feb-971997845200011NaN30-Jun-97$79,000.00$0.00P I F$0.0079000
8991599995573004FABRIC FARMSUPPER ARLINGTON150843221JPMORGAN CHASE BANK NATL ASSOCIL4527-Feb-971997606100000NaN30-Sep-97$70,000.00$0.00P I F$0.0070000
8991609995603000FABRIC FARMSCOLUMBUS150843221JPMORGAN CHASE BANK NATL ASSOCIL4527-Feb-971997606100030NaN31-Oct-97$85,000.00$0.00P I F$0.0085000
8991619995613003RADCO MANUFACTURING CO.,INC.SANTA MARIA30193455RABOBANK, NATIONAL ASSOCIATIONCA3327-Feb-97199710826100010NaN30-Sep-97$300,000.00$0.00P I F$0.00300000
8991629995973006MARUTAMA HAWAII, INC.HONOLULU80996830BANK OF HAWAIIHI027-Feb-9719976061000118-Mar-0031-Mar-97$75,000.00$0.00CHGOFF$46,383.0075000
8991639996003010PACIFIC TRADEWINDS FAN & LIGHTKAILUA80996734CENTRAL PACIFIC BANKHI027-Feb-971997481200010NaN31-May-97$30,000.00$0.00P I F$0.0030000